Low organisational justice and heavy drinking: a prospective cohort study

Objectives: To investigate whether low perceived organisational injustice predicts heavy drinking among employees. Methods: Data from the prospective occupational cohort study, the 10-Town Study, related to 15 290 Finnish public sector local government employees nested in 2432 work units, were used. Non-drinkers were excluded. Procedural, interactional and total organisational justice, heavy drinking (>=210 g of absolute alcohol per week) and other psychosocial factors were determined by means of questionnaire in 2000-2001 (phase 1) and 2004 (phase 2). Multilevel logistic regression analyses taking into account for the hierarchical structure of the data were conducted and adjustments were made for sex, age, socio-economic position, marital status, baseline heavy drinking, psychological distress and other psychosocial risk factors such as job strain and effort/reward imbalance. Results: After adjustments, participants who reported low procedural justice at phase 1 were about 1.2 times more likely to be heavy drinkers at phase 2 compared with their counterparts with high justice. Low perceived justice in interpersonal treatment and low perceived total organisational justice were associated with an elevated prevalence of heavy drinking only in the socio-demographics adjusted model. Conclusions: This is the first longitudinal study to show that low procedural justice is weakly associated with an increased likelihood of heavy drinking.

Use of posterior predictive assessments to evaluate model fit in multilevel logistic regression

Assessing the fit of a model is an important final step in any statistical analysis, but this is not straightforward when complex discrete response models are used. Cross validation and posterior predictions have been suggested as methods to aid model criticism. In this paper a comparison is made between four methods of model predictive assessment in the context of a three level logistic regression model for clinical mastitis in dairy cattle; cross validation, a prediction using the full posterior predictive distribution and two “mixed” predictive methods that incorporate higher level random effects simulated from the underlying model distribution. Cross validation is considered a gold standard method but is computationally intensive and thus a comparison is made between posterior predictive assessments and cross validation. The analyses revealed that mixed prediction methods produced results close to cross validation whilst the full posterior predictive assessment gave predictions that were over-optimistic (closer to the observed disease rates) compared with cross validation. A mixed prediction method that simulated random effects from both higher levels was best at identifying the outlying level two (farm-year) units of interest. It is concluded that this mixed prediction method, simulating random effects from both higher levels, is straightforward and may be of value in model criticism of multilevel logistic regression, a technique commonly used for animal health data with a hierarchical structure.